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ObjectivesTo investigate the serum IL-6 levels and their rate of change in predicting the mortality of critically ill patients with COVID-19 in Vietnam.DesignA single-centre, cross-sectional study.SettingAn Intensive Care Centre for the Treatment of Critically Ill Patients with COVID-19 in Ho Chi Minh City, Vietnam.ParticipantsWe included patients aged 18 years or older who were critically ill with COVID-19 and presented to the study centre from 30 July 2021 to 15 October 2021. We excluded patients who did not have serum IL-6 measurements between admission and the end of the first day.Primary outcome measuresThe primary outcome was hospital all-cause mortality.ResultsOf 90 patients, 41.1% were men, the median age was 60.5 years (Q1–Q3: 52.0–71.0), and 76.7% of patients died in the hospital. Elevated IL-6 levels were observed on admission (41.79 pg/mL; Q1–Q3: 20.68–106.27) and on the third day after admission (72.00 pg/mL; Q1–Q3: 26.98–186.50), along with a significant rate of change in IL-6 during that period (839.5%; SD: 2753.2). While admission IL-6 level (areas under the receiver operator characteristic curve (AUROC): 0.610 (95% CI: 0.459 to 0.761); cut-off value ≥15.8 pg/mL) and rate of change in IL-6 on the third day of admission (AUROC: 0.586 (95% CI: 0.420 to 0.751); cut-off value ≥−58.7%) demonstrated poor discriminatory ability in predicting hospital mortality, the third day IL-6 rate of change from admission ≥−58.7% (adjusted OR: 12.812; 95% CI: 2.104 to 78.005) emerged as an independent predictor of hospital mortality.ConclusionsThis study focused on a highly selected cohort of critically ill COVID-19 patients with a high IL-6 level and mortality rate. Despite the poor discriminatory value of admission IL-6 levels, the rate of change in IL-6 proved valuable in predicting mortality. To identify critically ill COVID-19 patients with the highest risk for mortality, monitoring the serial serum IL-6 measurements and observing the rate of change in serum IL-6 levels over time are needed.

Thanks to the exponential growth in computing power and vast amounts of data, artificial intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be ubiquitously adopted in our daily lives. Even though AI-powered systems have brought competitive advantages, the black-box nature makes them lack transparency and prevents them from explaining their decisions. This issue has motivated the introduction of explainable artificial intelligence (XAI), which promotes AI algorithms that can show their internal process and explain how they made decisions. The number of XAI research has increased significantly in recent years, but there lacks a unified and comprehensive review of the latest XAI progress. This review aims to bridge the gap by discovering the critical perspectives of the rapidly growing body of research associated with XAI. After offering the readers a solid XAI background, we analyze and review various XAI methods, which are grouped into (i) pre-modeling explainability, (ii) interpretable model, and (iii) post-modeling explainability. We also pay attention to the current methods that dedicate to interpret and analyze deep learning methods. In addition, we systematically discuss various XAI challenges, such as the trade-off between the performance and the explainability, evaluation methods, security, and policy. Finally, we show the standard approaches that are leveraged to deal with the mentioned challenges.

Xpert MTB/RIF Ultra (Xpert Ultra) might have higher sensitivity than its predecessor, Xpert MTB/RIF (Xpert), but its role in tuberculous meningitis diagnosis is uncertain. We aimed to compare Xpert Ultra with Xpert for the diagnosis of tuberculous meningitis in HIV-uninfected and HIV-infected adults. In this prospective, randomised, diagnostic accuracy study, adults (≥16 years) with suspected tuberculous meningitis from a single centre in Vietnam were randomly assigned to cerebrospinal fluid testing by either Xpert Ultra or Xpert at baseline and, if treated for tuberculous meningitis, after 3–4 weeks of treatment. Test performance (sensitivity, specificity, and positive and negative predictive values) was calculated for Xpert Ultra and Xpert and compared against clinical and mycobacterial culture reference standards. Analyses were done for all patients and by HIV status. Between Oct 16, 2017, and Feb 10, 2019, 205 patients were randomly assigned to Xpert Ultra (n=103) or Xpert (n=102). The sensitivities of Xpert Ultra and Xpert for tuberculous meningitis diagnosis against a reference standard of definite, probable, and possible tuberculous meningitis were 47·2% (95% CI 34·4–60·3; 25 of 53 patients) for Xpert Ultra and 39·6% (27·6–53·1; 21 of 53) for Xpert (p=0·56); specificities were 100·0% (95% CI 92·0–100·0; 44 of 44) and 100·0% (92·6–100·0; 48 of 48), respectively. In HIV-negative patients, the sensitivity of Xpert Ultra was 38·9% (24·8–55·1; 14 of 36) versus 22·9% (12·1–39·0; eight of 35) by Xpert (p=0·23). In HIV co-infected patients, the sensitivities were 64·3% (38·8–83·7; nine of 14) for Xpert Ultra and 76·9% (49·7–91·8; ten of 13) for Xpert (p=0·77). Negative predictive values were 61·1% (49·6–71·5) for Xpert Ultra and 60·0% (49·0–70·0) for Xpert. Against a reference standard of mycobacterial culture, sensitivities were 90·9% (72·2–97·5; 20 of 22 patients) for Xpert Ultra and 81·8% (61·5–92·7; 18 of 22) for Xpert (p=0·66); specificities were 93·9% (85·4–97·6; 62 of 66) and 96·9% (89·5–91·2; 63 of 65), respectively. Six (22%) of 27 patients had a positive test by Xpert Ultra after 4 weeks of treatment versus two (9%) of 22 patients by Xpert. Xpert Ultra was not statistically superior to Xpert for the diagnosis of tuberculous meningitis in HIV-uninfected and HIV-infected adults. A negative Xpert Ultra or Xpert test does not rule out tuberculous meningitis. New diagnostic strategies are urgently required. Wellcome Trust and the Foundation for Innovative New Diagnostics.

In this study, pH indicative films were successfully synthesized from hydrogels made by blending 1% poly(vinyl alcohol) (PVA) and 1% chitosan (CS) with anthocyanin (ATH) and sodium tripolyphosphate (STPP). Particularly, ATH extracted from red cabbage was used as the pH indicator, while STPP was utilized as the cross-linking agent to provide better mechanical properties of the cast films. FT-IR spectra confirmed the existence of the ATH in the cast films. Moreover, the tensile strength, the elongation-at-break, and the swelling indices of the cast films were measured. In general, these properties of pH indicative films were profoundly influenced by the compositions of PVA/CS and the STPP dosage applied in the hydrogels. For example, the tensile strength could change from 43.27 MPa on a film cast from pure PVA hydrogel to 29.89 MPa, if 35% of the PVA hydrogel was substituted with CS. The cast films were applied as a food wrap that could be used to monitor visually the quality of the enwrapped food via the color change of the film upon the variation in pH values of the enwrapped food. In practice, a sequential change in color was successfully observed on the pH indicative films partially enwrapping the pork belly, indicating the spoilage of the meat.

PurposeThe surging entrance of new mobile payment merchants into the growing market has prompted the need for an in-depth understanding of loyalty formation to retain customers. This study examines customers' loyalty generation process in mobile payment services by exploring the serial effect of cognitive drivers (i.e. brand awareness, perceived quality, brand image, perceived value and layout) on affective response, satisfaction and loyalty.Design/methodology/approachA survey using self-administered questionnaires was conducted. The data was collected from 370 consumers who have experience using mobile payment services in Vietnam. The data were submitted to partial least square structural equation modeling (PLS-SEM) and artificial neural networks (ANN) analysis.FindingsThe results indicated that all the proposed cognitive drivers show significant impacts on affective response, which, in turn, translates into satisfaction and loyalty. The post-hoc analysis revealed enjoyment as the vital affective response in determining satisfaction. Moreover, the multigroup analysis indicated that the relationship between affective response and satisfaction is stronger for the female group. In addition, the ANN's nonlinear result revealed complementary insight into the importance of cognitive drivers.OriginalityThe current study revealed both linear and nonlinear mechanisms that explicate the roles of cognitive drivers and affective responses in fostering loyalty toward mobile payment merchants. The findings add to the existing literature that emphasizes consumers' initial mobile payment adoption.

With enough advanced notice, dengue outbreaks can be mitigated. As a climate-sensitive disease, environmental conditions and past patterns of dengue can be used to make predictions about future outbreak risk. These predictions improve public health planning and decision-making to ultimately reduce the burden of disease. Past approaches to dengue forecasting have used seasonal climate forecasts, but the predictive ability of a system using different lead times in a year-round prediction system has been seldom explored. Moreover, the transition from theoretical to operational systems integrated with disease control activities is rare. We introduce an operational seasonal dengue forecasting system for Vietnam where Earth observations, seasonal climate forecasts, and lagged dengue cases are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to 6 months ahead. Bayesian spatiotemporal models were fit to 19 years (2002-2020) of dengue data at the province level across Vietnam. A superensemble of these models then makes probabilistic predictions of dengue incidence at various future time points aligned with key Vietnamese decision and planning deadlines. We demonstrate that the superensemble generates more accurate predictions of dengue incidence than the individual models it incorporates across a suite of time horizons and transmission settings. Using historical data, the superensemble made slightly more accurate predictions (continuous rank probability score [CRPS] = 66.8, 95% CI 60.6-148.0) than a baseline model which forecasts the same incidence rate every month (CRPS = 79.4, 95% CI 78.5-80.5) at lead times of 1 to 3 months, albeit with larger uncertainty. The outbreak detection capability of the superensemble was considerably larger (69%) than that of the baseline model (54.5%). Predictions were most accurate in southern Vietnam, an area that experiences semi-regular seasonal dengue transmission. The system also demonstrated added value across multiple areas compared to previous practice of not using a forecast. We use the system to make a prospective prediction for dengue incidence in Vietnam for the period May to October 2020. Prospective predictions made with the superensemble were slightly more accurate (CRPS = 110, 95% CI 102-575) than those made with the baseline model (CRPS = 125, 95% CI 120-168) but had larger uncertainty. Finally, we propose a framework for the evaluation of probabilistic predictions. Despite the demonstrated value of our forecasting system, the approach is limited by the consistency of the dengue case data, as well as the lack of publicly available, continuous, and long-term data sets on mosquito control efforts and serotype-specific case data. This study shows that by combining detailed Earth observation data, seasonal climate forecasts, and state-of-the-art models, dengue outbreaks can be predicted across a broad range of settings, with enough lead time to meaningfully inform dengue control. While our system omits some important variables not currently available at a subnational scale, the majority of past outbreaks could be predicted up to 3 months ahead. Over the next 2 years, the system will be prospectively evaluated and, if successful, potentially extended to other areas and other climate-sensitive disease systems.

This study addresses the growing importance of hand gesture recognition across diverse fields, such as industry, education, and healthcare, targeting the often-neglected needs of the deaf and dumb community. The primary objective is to improve communication between individuals, thereby enhancing the overall quality of life, particularly in the context of advanced healthcare. This paper presents a novel approach for real-time hand gesture recognition using bio-impedance techniques. The developed device, powered by a Raspberry Pi and connected to electrodes for impedance data acquisition, employs an impedance chip for data collection. To categorize hand gestures, Convolutional Neuron Network (CNN), XGBoost, and Random Forest were used. The model successfully recognized up to nine distinct gestures, achieving an average accuracy of 97.24% across ten subjects using a subject-dependent strategy, showcasing the efficacy of the bioimpedance-based system in hand gesture recognition. The promising results lay a foundation for future developments in nonverbal communication between humans and machines as it contributes to the advancement of technology for the benefit of individuals with hearing impairments, addressing a critical social need.

Oil spillage is one of the world’s biggest environmental problems and its various impacts included shifting the balance of the ecosystem, affecting marine animals, and inhibiting economical activities. Therefore, the efficient resolution of this issue is a topic of great interest. In this work, cellulose coconut peat cryogel (CCPC) is synthesized by freeze-drying technique with cellulose extracted from coconut peat and poly(vinyl alcohol) (PVA) as a binder. The CCPC is furthermore dip-coated in poly(dimethylsiloxane) (PDMS) to obtain PDMS-coated cellulose coconut peat cryogel (CCPC-P) with hydrophobic property for the studying of oil adsorption. The characteristics of CCPC and CCPC-P are evaluated by density and porosity, specific surface area following Brunauer–Emmett–Teller (BET) theory, scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), energy dispersive X-ray analysis (EDX), and water contact angle (WCA) measurements. Results showed that CCPC-10 with the mass ratios of cellulose to PVA 10:1 had the lowest density of 28.21 mg/cm 3 , highest porosity of 98.15 %. Furthermore, after coating with PDMS, the obtained hydrophobic CCPC-P10 had maximum adsorption capacities of up to 2.083 and 2.452 mg/mg for the static adsorption model and dynamic adsorption model, respectively. This indicates that coconut peat is a viable material for cryogel synthesis in oil adsorption applications.

There is ample support for developmental regulation of glioblastoma stem cells. To examine how cell lineage controls glioblastoma stem cell function, we present a cross-species epigenome analysis of mouse and human glioblastoma stem cells. We analyze and compare the chromatin-accessibility landscape of nine mouse glioblastoma stem cell cultures of three defined origins and 60 patient-derived glioblastoma stem cell cultures by assay for transposase-accessible chromatin using sequencing. This separates the mouse cultures according to cell of origin and identifies three human glioblastoma stem cell clusters that show overlapping characteristics with each of the mouse groups, and a distribution along an axis of proneural to mesenchymal phenotypes. The epigenetic-based human glioblastoma stem cell clusters display distinct functional properties and can separate patient survival. Cross-species analyses reveals conserved epigenetic regulation of mouse and human glioblastoma stem cells. We conclude that epigenetic control of glioblastoma stem cells primarily is dictated by developmental origin which impacts clinically relevant glioblastoma stem cell properties and patient survival. The epigenetic regulation of glioblastoma stem cell (GSC) function remains poorly understood. Here, the authors compare the chromatin accessibility landscape of GSC cultures from mice and patients and suggest that the epigenome of GSCs is cell lineage-regulated and could predict patient survival.

Computer vision plays a significant role in mobile robot navigation due to the wealth of information extracted from digital images. Mobile robots localize and move to the intended destination based on the captured images. Due to the complexity of the environment, obstacle avoidance still requires a complex sensor system with a high computational efficiency requirement. This study offers a real-time solution to the problem of extracting corridor scenes from a single image using a lightweight semantic segmentation model integrating with the quantization technique to reduce the numerous training parameters and computational costs. The proposed model consists of an FCN as the decoder and MobilenetV2 as the decoder (with multi-scale fusion). This combination allows us to significantly minimize computation time while achieving high precision. Moreover, in this study, we also propose to use the Balance Cross-Entropy loss function to handle diverse datasets, especially those with class imbalances and to integrate a number of techniques, for example, the Adam optimizer and Gaussian filters, to enhance segmentation performance. The results demonstrate that our model can outperform baselines across different datasets. Moreover, when being applied to practical experiments with a real mobile robot, the proposed model’s performance is still consistent, supporting the optimal path planning, allowing the mobile robot to efficiently and effectively avoid the obstacles.